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Found 7 papers, 0 papers with code
Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity
This paper presents a novel application of graph neural networks for modeling and estimating network heterogeneity.
Regression adjustment in randomized controlled trials with many covariates
By employing Neyman's (1923) finite population perspective, we propose a bias-corrected regression adjustment estimator using cross-fitting, and show that the proposed estimator has favorable properties over existing alternatives.
GLS under Monotone Heteroskedasticity
Our GLS estimator is shown to be asymptotically equivalent to the infeasible GLS estimator with knowledge of the conditional error variance, and involves only some tuning to trim boundary observations, not only for point estimation but also for interval estimation or hypothesis testing.
Conditional Likelihood Ratio Test with Many Weak Instruments
This paper extends validity of the conditional likelihood ratio (CLR) test developed by Moreira (2003) to instrumental variable regression models with unknown error variance and many weak instruments.
Isotonic propensity score matching
We propose a one-to-many matching estimator of the average treatment effect based on propensity scores estimated by isotonic regression.
Regression Discontinuity Design with Potentially Many Covariates
This paper studies the case of possibly high-dimensional covariates in the regression discontinuity design (RDD) analysis.
On Gaussian Approximation for M-Estimator
This study develops a non-asymptotic Gaussian approximation theory for distributions of M-estimators, which are defined as maximizers of empirical criterion functions.
Statistics Theory Statistics Theory
